Learning a Natural Language Interface with Neural Programmer
This work addresses the challenge of natural language querying for databases without requiring domain-specific annotations, offering a novel approach for users needing intuitive data access.
The paper tackles the problem of learning a natural language interface for database tables by developing a weakly supervised, end-to-end neural network model that induces programs from question-answer pairs, achieving 34.2% accuracy with a single model and 37.7% with an ensemble, which is competitive with the state-of-the-art at 37.1%.
Learning a natural language interface for database tables is a challenging task that involves deep language understanding and multi-step reasoning. The task is often approached by mapping natural language queries to logical forms or programs that provide the desired response when executed on the database. To our knowledge, this paper presents the first weakly supervised, end-to-end neural network model to induce such programs on a real-world dataset. We enhance the objective function of Neural Programmer, a neural network with built-in discrete operations, and apply it on WikiTableQuestions, a natural language question-answering dataset. The model is trained end-to-end with weak supervision of question-answer pairs, and does not require domain-specific grammars, rules, or annotations that are key elements in previous approaches to program induction. The main experimental result in this paper is that a single Neural Programmer model achieves 34.2% accuracy using only 10,000 examples with weak supervision. An ensemble of 15 models, with a trivial combination technique, achieves 37.7% accuracy, which is competitive to the current state-of-the-art accuracy of 37.1% obtained by a traditional natural language semantic parser.